Dentists' Most Common Practices when Selecting an Implant System.

PURPOSE: To report a comprehensive description of dental implant system selection practices among dentists practicing implantology worldwide. MATERIALS AND METHODS: An online questionnaire was designed and sent to members of 15 dental implant organizations. The survey questions addressed: dental implant system selection criteria, implant design variables, dentists' perspective to implant quality stamps, and dentists' satisfaction with their implant system(s). Responses were compiled and analyzed to determine correlation of responses using the chi-squared test (level of significance α ≤ 0.05). RESULTS: Out of 4264 invitations sent, a total of 2001 (response rate = 46.9%) dentists participated in the survey. Approximately half of survey respondents (48.7%) were general dentists. More than two-thirds of the survey respondents (72.5%) were performing both the surgical and prosthetic implant phases. Implant-abutment connections were the most important dental implant system selection criterion (84.7%), followed by scientific evidence available on the implant system (82.8%), and simplicity of prosthetic steps (81.4%). Patient preferences (19.8%) were rated as the least important aspect. Sandblasted large gritted acid etched implant surfaces (SLA) were the most commonly used implant surfaces (75.8%); fluoride coated surfaces were the least commonly used (15.4%). CONCLUSION: According to the results of this survey, most survey respondents practiced both surgical and prosthetic phases of dental implantology. The majority of survey respondents agreed on the importance of implant-abutment connections, scientific evidence available on implant systems, and simplicity of prosthetic steps when selecting implant systems.

Diffusion-weighted MRI and in-phase/opposed-phase sequences in the assessment of bone tumors.

PURPOSE: To evaluate the role of diffusion-weighted apparent diffusion coefficient (ADC) and in-phase/opposed-phase sequences in the differentiation of benign and malignant osseous tumors. MATERIALS AND METHODS: At 1.5T, routine sequences were compared to diffusion-weighted and in-phase/opposed-phase in 63 patients. Routine sequence magnetic resonance imaging (MRI) scoring, mean ADC value, and in-phase/opposed-phase signal intensity ratio (SIR) was obtained. Statistical analysis included significance, receiver operating characteristic (ROC), and linear correlation between the three parameters. RESULTS: In all, 38 patients had malignant tumors and 25 patients had benign tumors. Benign and malignant tumors showed different routine sequence scores (P < 0.001). Mean ADC of the benign lesions ranged 0.9-3.2 × 10(-3) mm(2) /sec of mean ± SD (1.9 ± 0.6). In malignant tumors, the mean ADC ranged 0.6-1.9 × 10(-3) mm(2) /sec of mean ± SD (1.1 ± 0.4) (P < 0.0001). There was a possible differentiation between malignant and benign tumors at a threshold of 1.1 × 10(-3) mm(2) /sec of sensitivity and specificity of 94.1% and 70.3%, respectively. SIR for benign tumors ranged 0.2-1.0 of mean ± SD (0.6 ± 0.3). For malignant lesions SIR ranged 0.4-1.2 of mean ± SD (0.8 ± 0.3). Benign and malignant tumors show statistically significant SIR at P < 0.022 with possible differentiation at a threshold of 0.75 of sensitivity and specificity of 70.3% and 76.5%, respectively. Simple linear correlation between both ADC and SIR was significant at P < 0.01 with correlation coefficient (r) = 0.45. CONCLUSION: Diffusion-weighted and in-phase/opposed-phase imaging might be used in addition to conventional MRI as a routine tool for differentiation of benign and malignant tumors. J. Magn. Reson. Imaging 2016;44:565-572.

Decision Support Systems in Oncology.

Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data-clinical, imaging, biologic, genetic, cost-to produce validated predictive models. DSSs compare the personalized probable outcomes-toxicity, tumor control, quality of life, cost effectiveness-of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders-clinicians, medical directors, medical insurers, patient advocacy groups-and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.

The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance.

According to the estimations of the World Health Organization and the International Agency for Research in Cancer, lung cancer is the most common cause of death from cancer worldwide. The last few years have witnessed a rise in the attention given to the use of clinical decision support systems in medicine generally and in cancer in particular. These can predict patients' likelihood of survival based on analysis of and learning from previously treated patients. The datasets that are mined for developing clinical decision support functionality are often incomplete, which adversely impacts the quality of the models developed and the decision support offered. Imputing missing data using a statistical analysis approach is a common method to addressing the missing data problem. This work investigates the effect of imputation methods for missing data in preparing a training dataset for a Non-Small Cell Lung Cancer survival prediction model using several machine learning algorithms. The investigation includes an assessment of the effect of imputation algorithm error on performance prediction and also a comparison between using a smaller complete real dataset or a larger dataset with imputed data. Our results show that even when the proportion of records with some missing data is very high (> 80%) imputation can lead to prediction models with an AUC (0.68-0.72) comparable to those trained with complete data records.

Reactive carpal synovitis: initial experience with MR imaging.

PURPOSE: To retrospectively evaluate the accuracy of various magnetic resonance (MR) imaging findings in the diagnosis of reactive carpal synovitis. MATERIALS AND METHODS: Institutional review board approval was obtained, and the need for informed consent was waived. This study was compliant with the Health Insurance Portability and Accountability Act. Thirty-five consecutive patients (19 male and 16 female patients; age range, 13-57 years) who underwent arthroscopy and MR imaging within 4 weeks of surgery were evaluated by two reviewers for the following potential findings of synovitis: (a) distention of the pisotriquetral recess by fluid, (b) distention of the radial and/or prestyloid recess, (c) synovial enhancement (in patients who received contrast material), (d) amount of dorsal capsule distention, and (e) the location of bone marrow edema, if any. The chi2 and paired t tests were used to assess these findings in patients with and patients without arthroscopically proved synovitis. The sensitivity, specificity, positive and negative predictive values, and accuracy of these findings in the detection of synovitis were calculated. RESULTS: Fluid in the pisotriquetral recess was seen in nine of the 14 patients with synovitis and five of the 21 patients without synovitis (P = .018). Distention of the radial and/or prestyloid recess was observed in six of the 14 patients with synovitis and two of the 21 patients without synovitis (P = .027). Among the 24 patients who received contrast material, synovial enhancement was seen in seven of eight patients with synovitis and three of 16 patients without synovitis (P = .002). The dorsal capsule measured 1-7 mm (mean, 3.07 mm) in the 14 patients with synovitis and 2-7 mm (mean, 3.76 mm) in the 21 patients without synovitis (P = .193). Although bone marrow edema was seen globally in similar frequencies (nine of 14 patients with synovitis, nine of 21 patients without synovitis), pisotriquetral bone marrow edema was seen only in patients with synovitis (two of nine patients). CONCLUSION: Fluid in the pisotriquetral recess, enhancing synovium, and, less commonly, pisotriquetral bone marrow edema are MR imaging findings that may help in the diagnosis of reactive carpal synovitis.

Lunate chondromalacia: evaluation of routine MRI sequences.

OBJECTIVE: Chondromalacia is a commonly encountered abnormality at arthroscopy and may be responsible for significant clinical symptoms and disability. In the wrist, the most common location for chondromalacia is the lunate bone. Consequently, we sought to study the accuracy of clinical MRI in the assessment of lunate articular cartilage. MATERIALS AND METHODS: MR images of 34 patients who underwent arthroscopy and had an MRI examination within 1 month of surgery were evaluated by two reviewers for the presence and location of lunate cartilage defects and subchondral edema. RESULTS: Lunate cartilage defects were seen on MRI in 10 of the 13 patients with chondromalacia, but these defects were also incorrectly noted in three of 21 of patients without chondromalacia. The visible locations for cartilage defects were the ulnar aspect of the proximal lunate bone (n = 3), radial aspect of the proximal lunate bone (n = 4), ulnar aspect of the distal lunate bone (n = 2), and radial aspect of the distal lunate bone (n = 1). Subchondral marrow edema was observed in six of the 10 patients with chondromalacia seen on MRI; in all six patients, the edema was seen in the same quadrant as the cartilage defect. Marrow edema was detected in one patient without chondromalacia. CONCLUSION: We conclude that lunate chondromalacia can be accurately assessed using routine MRI sequences, although there are occasional false-positive interpretations.